Cross-domain network assurance

ABSTRACT

Systems, methods, and computer-readable media for providing cross-domain assurance for networks in different network domains. In some embodiments, a method can include collecting first fabric data for a first network in a first network domain and second fabric data for a second network in a second network domain. The second fabric data for the second network can be normalized based on the first network domain to create normalized second fabric data. The first fabric data can then be correlated with the normalized second fabric data to create correlated fabric data. Subsequently, assurance can be provided across the first network in the first network domain and the second network in the second network domain using the correlated fabric data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation and claims priority to U.S.Non-Provisional Patent application Ser. No. 17/039,328, filed Sep. 30,2020 which is a continuation and claims priority to U.S. Non-Provisionalpatent application Ser. No. 16/002,981, filed on Jun. 7, 2018, the fulldisclosure of which is hereby expressly incorporated by reference intheir entireties.

TECHNICAL FIELD

The present technology pertains to providing network assurance and inparticular to providing network assurance across networks in differentnetwork domains. In particular, each domain may use a discrete set oftechnologies, protocols to implement forwarding between elements andendpoints that reside on these elements. The network domains may alsoimplement policy between network elements themselves, or betweenendpoints attached to said network elements using a discrete set oftechnologies.

BACKGROUND

Software-defined networks (SDNs) have been developed in order to improveperformance in networks and provide greater control in managingnetworks. SDNs can decouple network control and forwarding functions tocreate programmable network control. In turn, this can abstract anunderlying network infrastructure from applications and networkservices. This can allow for easy control and configuring of networkenvironments by network administrators.

Sensors and network tools can be utilized in SDNs to provide assurancein the SDNs. Specifically, sensors can be implemented in an SDN togather data for the SDN and network tools can model operation andbehaviors of the SDN based on data gathered by the sensor. Subsequently,events can be generated for the SDN using the gathered data and modelsin order to provide assurance in the SDN. Such sensors and network toolscan provide assurance on a per-SDN basis. Specifically, such sensors andnetwork tools can provide assurance for a network in a specific networkdomain without respect to communications and interactions between thenetwork and other networks in different network domains. This isproblematic, as network traffic often times originates from one networkdomain and extends into another network in a different network domain.However, as assurance is only provided on a per-network domain basis,the network traffic is only assured with respect to a specific networkthat a portion of the network traffic passes through. More specifically,the network traffic is not assured across multiple networks, e.g. SDNs,in multiple domains that the network traffic ultimately spans across.There therefore exist needs for normalizing forwarding, policy and othersemantics in each SDN and providing assurance across multiple networks,e.g. SDNs, across different network domains.

Further, in typical SDNs, policies can be configured which ultimatelydeploys rules in switches to enforce control on underlying traffic. Inparticular, policies can be deployed to controllers and/or identityservices engines where the policies can be used to deploy rules forcontrolling underlying traffic. As part of providing assurance, suchpolicies can be modeled to ensure that desired, e.g. intent-based,traffic control is actually being enforced in the SDN. As assurance isprovided on a per-network basis, policy checks are performedirrespective of policies that exist in networks in different domains.Specifically, SDNs in different network domains that are communicatingwith each other can have conflicting or otherwise incompatible policies.In turn, providing assurance on a per-network basis can lead to failuresin recognizing the conflicts between the policies. There therefore existneeds for providing policy assurance across multiple networks, e.g.SDNs, in different network domains.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIGS. 1A and 1B illustrate example network environments;

FIG. 2A illustrates an example object model for a network;

FIG. 2B illustrates an example object model for a tenant object in theexample object model from FIG. 2A;

FIG. 2C illustrates an example association of various objects in theexample object model from FIG. 2A;

FIG. 2D illustrates a schematic diagram of example models forimplementing the example object model from FIG. 2A;

FIG. 3A illustrates an example network assurance appliance;

FIG. 3B illustrates an example system for network assurance;

FIG. 3C illustrates a schematic diagram of an example system for staticpolicy analysis in a network.

FIG. 4 illustrates an example method embodiment for network assurance;

FIG. 5 illustrates an environment for providing cross-domain assurance;

FIG. 6 illustrates a flowchart for an example method of providingcross-domain assurance;

FIG. 7 shows a screen shot of an interface showing allowed cross-domainaccess as part of providing cross-domain assurance;

FIG. 8 illustrates an example network device in accordance with variousembodiments; and

FIG. 9 illustrates an example computing device in accordance withvarious embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which may beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms may be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Overview

A method can include collecting first fabric data for a first network ina first network domain and second fabric data for a second network in asecond network domain. The second fabric data for the second network canbe normalized based on the first network domain to create normalizedsecond fabric data. Further, the first fabric data can be correlatedwith the normalized second fabric data to create correlated fabric data.Subsequently, assurance can be provided across the first network in thefirst network domain and the second network in the second network domainusing the correlated fabric data.

A system can collect first fabric data for a first network in a firstnetwork domain and second fabric data for a second network in a secondnetwork domain. The system can also normalize the second fabric data forthe second network in the second network domain based on the firstnetwork domain to create normalized second fabric data. Further, thesystem can correlate the first fabric data with the normalized secondfabric data to create correlated fabric data to provide assurance acrossthe first network in the first network domain and the second network inthe second network domain.

A system can query identity management platforms to obtain mappings ofendpoint(s) to security group(s).

A system can provide a framework to map business level intent (E.g. PCIservers should not talk to non-PCI endpoints) using a tagging mechanismby allowing for mapping of operator or administrator defined tags tosecurity group(s) obtained from the identity management platform or aconfiguration management database (CMDB). [E.g. The assurance system maylet the operator define a tag of “PCI servers” for all endpoints in asubnet range (like 10.1.1.0/24) or all security groups with a certainname (like security group name contains “prod_web_”]

A system can collect first fabric data for a first network in a firstnetwork domain and second fabric data for a second network in a secondnetwork domain. The first network domain and the second network domaincan be different network domain types. The system can normalize thesecond fabric data for the second network in the second network domainbased on the first network domain to create normalized second fabricdata. The system can then correlate the first fabric data with thenormalized second fabric data to create correlated fabric data.Subsequently, the system can provide assurance across the first networkin the first network domain and the second network in the second networkdomain using the correlated fabric data.

A system can cross-reference information from the identity managementplatform with normalized information from both network domains todetermine the sanctity of this data across the identity managementsystem and both network domains is consistent.

A system can provide alerting to operator(s) and administrator(s) of theidentity management system and of the network domain(s) with specificcorrective actions in each domain to be taken to resolve any consistencyissues.

A system can provide the ability for end users to consume this datausing simple query terms like ‘Can A talk to B’ [where A is an endpointin the datacenter domain, B is an endpoint in a different SDN domain]without having to understand, extrapolate and interpret the intricaciesof each SDN domain.

A system can provide insight into exact semantics (e.g. forwarding,policy, etc..) that may be causing communication (or lack thereof)between two or more endpoints in each domain and suggest specific stepsto be followed by the operator(s) or administrator(s) of each domain toeither permit or restrict traffic flow between these endpoints

A system can consume business level intent (e.g. PCI servers should nottalk to non-PCI users). Glean mappings of PCI, non-PCI endpoints from apolicy, posture verification engine. Perform tagging of these endpointsbased on user defined policies. Perform cross-domain co-relation acrossthe identity management system (e.g. Cisco ISE) and various SDN domains(E.g. Datacenter, WAN, Campus) when intent is violated and provide anotification to the operator(s) and administrator(s) of relevant domainson business level intent being violated.

Example Embodiments

The disclosed technology addresses the need in the art for providingnetwork assurance. The present technology involves system, methods, andcomputer-readable media for providing network assurance across networksin different network domains. The present technology will be describedin the following disclosure as follows.

The discussion begins with an introductory discussion of networkassurance and a description of example computing environments, asillustrated in FIGS. 1A and 1B. A discussion of network models fornetwork assurance, as shown in FIGS. 2A through 2D, and networkassurance systems and methods, as shown in FIGS. 3A-C and 4 will thenfollow. The discussion continues with a description and examples ofproviding cross-domain assurance, as shown in FIGS. 5-7. The discussionconcludes with a description of an example network device, asillustrated in FIG. 8, and an example computing device, as illustratedin FIG. 9, including example hardware components suitable for hostingsoftware applications and performing computing operations. Thedisclosure now turns to an introductory discussion of network assurance.

Network assurance is the guarantee or determination that the network isbehaving as intended by the network operator and has been configuredproperly (e.g., the network is doing network and individual networkelements (e.g., switches, routers, applications, resources, etc.).However, often times, the configurations, policies, etc., defined by anetwork operator are incorrect or not accurately reflected in the actualbehavior of the network. For example, a network operator specifies aconfiguration A for one or more types of traffic but later finds outthat the network is actually applying configuration B to that traffic orotherwise processing that traffic in a manner that is inconsistent withconfiguration A. This can be a result of many different causes, such ashardware errors, software bugs, varying priorities, configurationconflicts, misconfiguration of one or more settings, improper rulerendering by devices, unexpected errors or events, software upgrades,configuration changes, failures, etc. As another example, a networkoperator implements configuration C but one or more other configurationsresult in the network behaving in a manner that is inconsistent with theintent reflected by the implementation of configuration C. For example,such a situation can result when configuration C conflicts with otherconfigurations in the network.

The approaches herein can provide network assurance by modeling variousaspects of the network and/or performing consistency checks as well asother network assurance checks. The network assurance approaches hereincan be implemented in various types of networks, including a privatenetwork, such as a local area network (LAN); an enterprise network; astandalone or traditional network, such as a data center network; anetwork including a physical or underlay layer and a logical or overlaylayer, such as a VXLAN or SDN (e.g., Application Centric Infrastructure(ACI), Amazon AWS VPCs, VXLAN EVPN based datacenter fabrics, VMware NSXnetworks); etc.

Network models can be constructed for a network and implemented fornetwork assurance. A network model can provide a representation of oneor more aspects of a network, including, without limitation thenetwork's policies, configurations, requirements, security, routing,topology, applications, hardware, filters, contracts, access controllists, infrastructure, etc. As will be further explained below,different types of models can be generated for a network.

Such models can be implemented to ensure that the behavior of thenetwork will be consistent (or is consistent) with the intended behaviorreflected through specific configurations (e.g., policies, settings,definitions, etc.) implemented by the network operator. Unliketraditional network monitoring, which involves sending and analyzingdata packets and observing network behavior, network assurance can beperformed through modeling without necessarily ingesting packet data ormonitoring traffic or network behavior. This can result in foresight,insight, and hindsight: problems can be prevented before they occur,identified when they occur, and fixed immediately after they occur.

Thus, network assurance can involve modeling properties of the networkto deterministically predict the behavior of the network. The networkcan be determined to be healthy if the model(s) indicate proper behavior(e.g., no inconsistencies, conflicts, errors, etc.). The network can bedetermined to be functional, but not fully healthy, if the modelingindicates proper behavior but some inconsistencies. The network can bedetermined to be non-functional and not healthy if the modelingindicates improper behavior and errors. If inconsistencies or errors aredetected by the modeling, a detailed analysis of the correspondingmodel(s) can allow one or more underlying or root problems to beidentified with great accuracy.

The modeling can consume numerous types of smart events which model alarge amount of behavioral aspects of the network. Smart events canimpact various aspects of the network, such as underlay services,overlay services, tenant connectivity, tenant security, tenant endpoint(EP) mobility, tenant policy, tenant routing, resources, etc. Further,the modeling can consumer numerous types of smart events which model alarge amount of behavioral aspects across networks, e.g. as part ofproviding cross-domain assurance for the networks.

Having described various aspects of network assurance, the disclosurenow turns to a discussion of example network environments for networkassurance.

FIG. 1A illustrates a diagram of an example Network Environment 100,such as a data center. The Network Environment 100 can include a Fabric120 which can represent the physical layer or infrastructure (e.g.,underlay) of the Network Environment 100. Fabric 120 can include Spines102 (e.g., spine routers or switches) and Leafs 104 (e.g., leaf routersor switches) which can be interconnected for routing or switchingtraffic in the Fabric 120. Spines 102 can interconnect Leafs 104 in theFabric 120, and Leafs 104 can connect the Fabric 120 to an overlay orlogical portion of the Network Environment 100, which can includeapplication services, servers, virtual machines, containers, endpoints,etc. Thus, network connectivity in the Fabric 120 can flow from Spines102 to Leafs 104, and vice versa. The interconnections between Leafs 104and Spines 102 can be redundant (e.g., multiple interconnections) toavoid a failure in routing. In some embodiments, Leafs 104 and Spines102 can be fully connected, such that any given Leaf is connected toeach of the Spines 102, and any given Spine is connected to each of theLeafs 104. Leafs 104 can be, for example, top-of-rack (“ToR”) switches,aggregation switches, gateways, ingress and/or egress switches, provideredge devices, and/or any other type of routing or switching device.

Leafs 104 can be responsible for routing and/or bridging tenant orcustomer packets and applying network policies or rules. Networkpolicies and rules can be driven by one or more Controllers 116, and/orimplemented or enforced by one or more devices, such as Leafs 104. Leafs104 can connect other elements to the Fabric 120. For example, Leafs 104can connect Servers 106, Hypervisors 108, Virtual Machines (VMs) 110,Applications 112, Network Device 114, etc., with Fabric 120. Suchelements can reside in one or more logical or virtual layers ornetworks, such as an overlay network. In some cases, Leafs 104 canencapsulate and decapsulate packets to and from such elements (e.g.,Servers 106) in order to enable communications throughout NetworkEnvironment 100 and Fabric 120. Leafs 104 can also provide any otherdevices, services, tenants, or workloads with access to Fabric 120. Insome cases, Servers 106 connected to Leafs 104 can similarly encapsulateand decapsulate packets to and from Leafs 104. For example, Servers 106can include one or more virtual switches or routers or tunnel endpointsfor tunneling packets between an overlay or logical layer hosted by, orconnected to, Servers 106 and an underlay layer represented by Fabric120 and accessed via Leafs 104.

Applications 112 can include software applications, services,containers, appliances, functions, service chains, etc. For example,Applications 112 can include a firewall, a database, a CDN server, anIDS/IPS, a deep packet inspection service, a message router, a virtualswitch, etc. An application from Applications 112 can be distributed,chained, or hosted by multiple endpoints (e.g., Servers 106, VMs 110,etc.), or may run or execute entirely from a single endpoint.

VMs 110 can be virtual machines hosted by Hypervisors 108 or virtualmachine managers running on Servers 106. VMs 110 can include workloadsrunning on a guest operating system on a respective server. Hypervisors108 can provide a layer of software, firmware, and/or hardware thatcreates, manages, and/or runs the VMs 110. Hypervisors 108 can allow VMs110 to share hardware resources on Servers 106, and the hardwareresources on Servers 106 to appear as multiple, separate hardwareplatforms. Moreover, Hypervisors 108 on Servers 106 can host one or moreVMs 110.

In some cases, VMs 110 and/or Hypervisors 108 can be migrated to otherServers 106. Servers 106 can similarly be migrated to other locations inNetwork Environment 100. For example, a server connected to a specificleaf can be changed to connect to a different or additional leaf. Suchconfiguration or deployment changes can involve modifications tosettings, configurations and policies that are applied to the resourcesbeing migrated as well as other network components.

In some cases, one or more Servers 106, Hypervisors 108, and/or VMs 110can represent or reside in a tenant or customer space. Tenant space caninclude workloads, services, applications, devices, networks, and/orresources that are associated with one or more clients or subscribers.Accordingly, traffic in Network Environment 100 can be routed based onspecific tenant policies, spaces, agreements, configurations, etc.Moreover, addressing can vary between one or more tenants. In someconfigurations, tenant spaces can be divided into logical segmentsand/or networks and separated from logical segments and/or networksassociated with other tenants. Addressing, policy, security andconfiguration information between tenants can be managed by Controllers116, Servers 106, Leafs 104, etc.

Configurations in Network Environment 100 can be implemented at alogical level, a hardware level (e.g., physical), and/or both. Forexample, configurations can be implemented at a logical and/or hardwarelevel based on endpoint or resource attributes, such as endpoint typesand/or application groups or profiles, through a software-definednetwork (SDN) framework (e.g., Application-Centric Infrastructure (ACI)or VMWARE NSX). To illustrate, one or more administrators can defineconfigurations at a logical level (e.g., application or software level)through Controllers 116, which can implement or propagate suchconfigurations through Network Environment 100. In some examples,Controllers 116 can be Application Policy Infrastructure Controllers(APICs) in an ACI framework. In other examples, Controllers 116 can beone or more management components for associated with other SDNsolutions, such as NSX Managers.

Such configurations can define rules, policies, priorities, protocols,attributes, objects, etc., for routing and/or classifying traffic inNetwork Environment 100. For example, such configurations can defineattributes and objects for classifying and processing traffic based onEndpoint Groups (EPGs), Security Groups (SGs), VM types, bridge domains(BDs), virtual routing and forwarding instances (VRFs), tenants,priorities, firewall rules, etc. Other example network objects andconfigurations are further described below. Traffic policies and rulescan be enforced based on tags, attributes, or other characteristics ofthe traffic, such as protocols associated with the traffic, EPGsassociated with the traffic, SGs associated with the traffic, networkaddress information associated with the traffic, etc. Such policies andrules can be enforced by one or more elements in Network Environment100, such as Leafs 104, Servers 106, Hypervisors 108, Controllers 116,etc. As previously explained, Network Environment 100 can be configuredaccording to one or more particular software-defined network (SDN)solutions, such as CISCO ACI or VMWARE NSX. These example SDN solutionsare briefly described below. ACI can provide an application-centric orpolicy-based solution through scalable distributed enforcement. ACIsupports integration of physical and virtual environments under adeclarative configuration model for networks, servers, services,security, requirements, etc. For example, the ACI framework implementsEPGs, which can include a collection of endpoints or applications thatshare common configuration requirements, such as security, QoS,services, etc. Endpoints can be virtual/logical or physical devices,such as VMs, containers, hosts, or physical servers that are connectedto Network Environment 100. Endpoints can have one or more attributessuch as a VM name, guest OS name, a security tag, application profile,etc. Application configurations can be applied between EPGs, instead ofendpoints directly, in the form of contracts. Leafs 104 can classifyincoming traffic into different EPGs. The classification can be basedon, for example, a network segment identifier such as a VLAN ID, VXLANNetwork Identifier (VNID), NVGRE Virtual Subnet Identifier (VSID), MACaddress, IP address, etc.

In some cases, classification in the ACI fabric can be implemented byApplication Virtual Switches (AVS), which can run on a host, such as aserver or switch. For example, an AVS can classify traffic based onspecified attributes, and tag packets of different attribute EPGs withdifferent identifiers, such as network segment identifiers (e.g., VLANID). Finally, Leafs 104 can tie packets with their attribute EPGs basedon their identifiers and enforce policies, which can be implementedand/or managed by one or more Controllers 116. Leaf 104 can classify towhich EPG the traffic from a host belongs and enforce policiesaccordingly.

Another example SDN solution is based on VMWARE NSX. With VMWARE NSX,hosts can run a distributed firewall (DFW) which can classify andprocess traffic. Consider a case where three types of VMs, namely,application, database and web VMs, are put into a single layer-2 networksegment. Traffic protection can be provided within the network segmentbased on the VM type. For example, HTTP traffic can be allowed among webVMs, and disallowed between a web VM and an application or database VM.To classify traffic and implement policies, VMWARE NSX can implementsecurity groups, which can be used to group the specific VMs (e.g., webVMs, application VMs, database VMs). DFW rules can be configured toimplement policies for the specific security groups. To illustrate, inthe context of the previous example, DFW rules can be configured toblock HTTP traffic between web, application, and database securitygroups.

Returning now to FIG. 1A, Network Environment 100 can deploy differenthosts via Leafs 104, Servers 106, Hypervisors 108, VMs 110, Applications112, and Controllers 116, such as VMWARE ESXi hosts, WINDOWS HYPER-Vhosts, bare metal physical hosts, etc. Network Environment 100 mayinteroperate with a variety of Hypervisors 108, Servers 106 (e.g.,physical and/or virtual servers), SDN orchestration platforms, etc.Network Environment 100 may implement a declarative model to allow itsintegration with application design and holistic network policy.

Controllers 116 can provide centralized access to fabric information,application configuration, resource configuration, application-levelconfiguration modeling for a software-defined network (SDN)infrastructure, integration with management systems or servers, etc.Controllers 116 can form a control plane that interfaces with anapplication plane via northbound APIs and a data plane via southboundAPIs.

As previously noted, Controllers 116 can define and manageapplication-level model(s) for configurations in Network Environment100. In some cases, application or device configurations can also bemanaged and/or defined by other components in the network. For example,a hypervisor or virtual appliance, such as a VM or container, can run aserver or management tool to manage software and services in NetworkEnvironment 100, including configurations and settings for virtualappliances.

As illustrated above, Network Environment 100 can include one or moredifferent types of SDN solutions, hosts, etc. For the sake of clarityand explanation purposes, various examples in the disclosure will bedescribed with reference to an ACI framework, and Controllers 116 may beinterchangeably referenced as controllers, APICs, or APIC controllers.However, it should be noted that the technologies and concepts hereinare not limited to ACI solutions and may be implemented in otherarchitectures and scenarios, including other SDN solutions as well asother types of networks which may not deploy an SDN solution.

Further, as referenced herein, the term “hosts” can refer to Servers 106(e.g., physical or logical), Hypervisors 108, VMs 110, containers (e.g.,Applications 112), etc., and can run or include any type of server orapplication solution. Non-limiting examples of “hosts” can includevirtual switches or routers, such as distributed virtual switches (DVS),application virtual switches (AVS), vector packet processing (VPP)switches; VCENTER and NSX MANAGERS; bare metal physical hosts; HYPER-Vhosts; VMs; DOCKER Containers; etc.

FIG. 1B illustrates another example of Network Environment 100. TheNetwork Environment 100 shown in FIG. 1B can represent a DNA network ofan enterprise, e.g. a campus network. In this example, NetworkEnvironment 100 includes Endpoints 122 connected to Fabric Edge Nodes104 in Fabric 120, which are connected to Fabric Border Nodes 102 inFabric 120. Fabric Border Nodes 102 can connect traditional layer 3networks or different fabric domains to an enterprise fabric domain.Fabric Edge Nodes 104 lie at the perimeter of the Fabric 120 and canserve as the first points of attachment of policy. For example, FabricEdge Nodes 104 can admit, encapsulate, decapsulate, and forward trafficfrom Endpoints 122. Fabric Edge Nodes 104 can connect to Endpoints 122through an intermediate Layer 2 network, e.g. through a wireless accesspoint.

Endpoints 122 can be physical and/or logical or virtual entities, suchas servers, clients, VMs, hypervisors, software containers,applications, resources, network devices, workloads, etc. For example,an Endpoint 122 can be an object that represents a physical device(e.g., server, client, switch, etc.), an application (e.g., webapplication, database application, etc.), a logical or virtual resource(e.g., a virtual switch, a virtual service appliance, a virtualizednetwork function (VNF), a VM, a service chain, etc.), a containerrunning a software resource (e.g., an application, an appliance, a VNF,a service chain, etc.), storage, a workload or workload engine, etc.Endpoints 122 can have an address (e.g., an identity), a location (e.g.,host, network segment, virtual routing and forwarding (VRF) instance,domain, etc.), one or more attributes (e.g., name, type, version, patchlevel, OS name, OS type, etc.), a tag (e.g., security tag), a profile,etc. Endpoints 122 can also include physical devices in the NetworkEnvironment 100, e.g. cameras, sensors, PCs, and laptops.

Endpoints 122 can be associated with respective Security Groups 118.Security Groups 118 can be logical entities containing endpoints(physical and/or logical or virtual) grouped together according to oneor more attributes, such as endpoint type (e.g., VM type, workload type,application type, etc.), one or more requirements (e.g., policyrequirements, security requirements, QoS requirements, customerrequirements, resource requirements, etc.), a resource name (e.g., VMname, application name, etc.), a profile, platform or operating system(OS) characteristics (e.g., OS type or name including guest and/or hostOS, etc.), an associated network or tenant, one or more policies, a tag,etc. For example, a security group can be an object representing acollection of endpoints grouped together. To illustrate, Security Group1 can contain student endpoints, Security Group 2 can contain facultyendpoints, Security Group 3 can contain network administrator endpoints,Logical Group N can contain different student group endpoints, etc. Insome examples, Security Groups 118 are EPGs in a SDN environment, e.g. aDNA campus network.

Traffic to and/or from Endpoints 122 can be classified, processed,managed, etc., based on Security Groups 118. For example, SecurityGroups 118 can be used to classify traffic to or from Endpoints 122,apply policies to traffic to or from Endpoints 122, define relationshipsbetween Endpoints 122, define roles of Endpoints 122 (e.g., whether anendpoint consumes or provides a service, etc.), apply rules to trafficto or from Endpoints 122, apply filters or access control lists (ACLs)to traffic to or from Endpoints 122, define communication paths fortraffic to or from Endpoints 122, enforce requirements associated withEndpoints 122, implement security and other configurations associatedwith Endpoints 122, etc.

FIG. 2A illustrates a diagram of an example Management Information Model200 for an SDN network, such as Network Environment 100. The followingdiscussion of Management Information Model 200 references various termswhich shall also be used throughout the disclosure. Accordingly, forclarity, the disclosure shall first provide below a list of terminology,which will be followed by a more detailed discussion of ManagementInformation Model 200.

As used herein, an “Alias” can refer to a changeable name for a givenobject. Thus, even if the name of an object, once created, cannot bechanged, the Alias can be a field that can be changed.

As used herein, the term “Aliasing” can refer to a rule (e.g.,contracts, policies, configurations, etc.) that overlaps one or moreother rules. For example, Contract 1 defined in a logical model of anetwork can be said to be aliasing Contract 2 defined in the logicalmodel of the network if Contract 1 overlaps Contract 1. In this example,by aliasing Contract 2, Contract 1 may render Contract 2 redundant orinoperable. For example, if Contract 1 has a higher priority thanContract 2, such aliasing can render Contract 2 redundant based onContract 1's overlapping and higher priority characteristics.

As used herein, the term “APIC” can refer to one or more controllers(e.g., Controllers 116) in an ACI framework. The APIC can provide aunified point of automation and management, policy programming,application deployment, health monitoring for an ACI multitenant fabric.The APIC can be implemented as a single controller, a distributedcontroller, or a replicated, synchronized, and/or clustered controller.

As used herein, the term “BDD” can refer to a binary decision tree . Abinary decision tree can be a data structure representing functions,such as Boolean functions.

As used herein, the term “BD” can refer to a bridge domain. A bridgedomain can be a set of logical ports that share the same flooding orbroadcast characteristics. Like a virtual LAN (VLAN), bridge domains canspan multiple devices. A bridge domain can be a L2 (Layer 2) construct.

As used herein, a “Consumer” can refer to an endpoint, resource, and/orEPG that consumes a service.

As used herein, a “Context” can refer to an L3 (Layer 3) address domainthat allows multiple instances of a routing table to exist and worksimultaneously. This increases functionality by allowing network pathsto be segmented without using multiple devices. Non-limiting examples ofa context or L3 address domain can include a Virtual Routing andForwarding (VRF) instance, a private network, and so forth.

As used herein, the term “Contract” can refer to rules or configurationsthat specify what and how communications in a network are conducted(e.g., allowed, denied, filtered, processed, etc.). In an ACI network,contracts can specify how communications between endpoints and/or EPGstake place. In some examples, a contract can provide rules andconfigurations akin to an Access Control List (ACL).

As used herein, the term “Distinguished Name” (DN) can refer to a uniquename that describes an object, such as an MO, and locates its place inManagement Information Model 200. In some cases, the DN can be (orequate to) a Fully Qualified Domain Name (FQDN).

As used herein, the term “Endpoint Group” (EPG) can refer to a logicalentity or object associated with a collection or group of endpoints aspreviously described with reference to FIG. 1B.

As used herein, the term “Filter” can refer to a parameter orconfiguration for allowing communications. For example, in a whitelistmodel where all communications are blocked by default, a communicationmust be given explicit permission to prevent such communication frombeing blocked. A filter can define permission(s) for one or morecommunications or packets. A filter can thus function similar to an ACLor Firewall rule. In some examples, a filter can be implemented in apacket (e.g., TCP/IP) header field, such as L3 protocol type, L4 (Layer4) ports, and so on, which is used to allow inbound or outboundcommunications between endpoints or EPGs, for example.

As used herein, the term “L2 Out” can refer to a bridged connection. Abridged connection can connect two or more segments of the same networkso that they can communicate. In an ACI framework, an L2 out can be abridged (Layer 2) connection between an ACI fabric (e.g., Fabric 120)and an outside Layer 2 network, such as a switch.

As used herein, the term “L3 Out” can refer to a routed connection. Arouted Layer 3 connection uses a set of protocols that determine thepath that data follows in order to travel across networks from itssource to its destination. Routed connections can perform forwarding(e.g., IP forwarding) according to a protocol selected, such as BGP(border gateway protocol), OSPF (Open Shortest Path First), EIGRP(Enhanced Interior Gateway Routing Protocol), etc.

As used herein, the term “Managed Object” (MO) can refer to an abstractrepresentation of objects that are managed in a network (e.g., NetworkEnvironment 100). The objects can be concrete objects (e.g., a switch,server, adapter, etc.), or logical objects (e.g., an applicationprofile, an EPG, a fault, etc.). The MOs can be network resources orelements that are managed in the network. For example, in an ACIenvironment, an MO can include an abstraction of an ACI fabric (e.g.,Fabric 120) resource.

As used herein, the term “Management Information Tree” (MIT) can referto a hierarchical management information tree containing the MOs of asystem. For example, in ACI, the MIT contains the MOs of the ACI fabric(e.g., Fabric 120). The MIT can also be referred to as a ManagementInformation Model (MIM), such as Management Information Model 200.

As used herein, the term “Policy” can refer to one or morespecifications for controlling some aspect of system or networkbehavior. For example, a policy can include a named entity that containsspecifications for controlling some aspect of system behavior. Toillustrate, a Layer 3 Outside Network Policy can contain the BGPprotocol to enable BGP routing functions when connecting Fabric 120 toan outside Layer 3 network.

As used herein, the term “Profile” can refer to the configurationdetails associated with a policy. For example, a profile can include anamed entity that contains the configuration details for implementingone or more instances of a policy. To illustrate, a switch node profilefor a routing policy can contain the switch-specific configurationdetails to implement the BGP routing protocol.

As used herein, the term “Provider” refers to an object or entityproviding a service. For example, a provider can be an EPG that providesa service.

As used herein, the term “Subject” refers to one or more parameters in acontract for defining communications. For example, in ACI, subjects in acontract can specify what information can be communicated and how.Subjects can function similar to ACLs.

As used herein, the term “Tenant” refers to a unit of isolation in anetwork. For example, a tenant can be a secure and exclusive virtualcomputing environment. In ACI, a tenant can be a unit of isolation froma policy perspective, but does not necessarily represent a privatenetwork. Indeed, ACI tenants can contain multiple private networks(e.g., VRFs). Tenants can represent a customer in a service providersetting, an organization or domain in an enterprise setting, or just agrouping of policies.

As used herein, the term “VRF” refers to a virtual routing andforwarding instance. The VRF can define a Layer 3 address domain thatallows multiple instances of a routing table to exist and worksimultaneously. This increases functionality by allowing network pathsto be segmented without using multiple devices. Also known as a contextor private network.

Having described various terms used herein, the disclosure now returnsto a discussion of Management Information Model (MIM) 200 in FIG. 2A. Aspreviously noted, MIM 200 can be a hierarchical management informationtree or MIT. Moreover, MIM 200 can be managed and processed byControllers 116, such as APICs in an ACI. Controllers 116 can enable thecontrol of managed resources by presenting their manageablecharacteristics as object properties that can be inherited according tothe location of the object within the hierarchical structure of themodel.

The hierarchical structure of MIM 200 starts with Policy Universe 202 atthe top (Root) and contains parent and child nodes 116, 204, 206, 208,210, 212. Nodes 116, 202, 204, 206, 208, 210, 212 in the tree representthe managed objects (MOs) or groups of objects. Each object in thefabric (e.g., Fabric 120) has a unique distinguished name (DN) thatdescribes the object and locates its place in the tree. The Nodes 116,202, 204, 206, 208, 210, 212 can include the various MOs, as describedbelow, which contain policies that govern the operation of the system.

Controllers 116

Controllers 116 (e.g., APIC controllers) can provide management, policyprogramming, application deployment, and health monitoring for Fabric120.

Node 204

Node 204 includes a tenant container for policies that enable anadministrator to exercise domain-based access control. Non-limitingexamples of tenants can include:

User tenants defined by the administrator according to the needs ofusers. They contain policies that govern the operation of resources suchas applications, databases, web servers, network-attached storage,virtual machines, and so on.

The common tenant is provided by the system but can be configured by theadministrator. It contains policies that govern the operation ofresources accessible to all tenants, such as firewalls, load balancers,Layer 4 to Layer 7 services, intrusion detection appliances, and so on.

The infrastructure tenant is provided by the system but can beconfigured by the administrator. It contains policies that govern theoperation of infrastructure resources such as the fabric overlay (e.g.,VXLAN). It also enables a fabric provider to selectively deployresources to one or more user tenants. Infrastructure tenant polices canbe configurable by the administrator.

The management tenant is provided by the system but can be configured bythe administrator. It contains policies that govern the operation offabric management functions used for in-band and out-of-bandconfiguration of fabric nodes. The management tenant contains a privateout-of-bound address space for the Controller/Fabric internalcommunications that is outside the fabric data path that provides accessthrough the management port of the switches. The management tenantenables discovery and automation of communications with virtual machinecontrollers.

Node 206

Node 206 can contain access policies that govern the operation of switchaccess ports that provide connectivity to resources such as storage,compute, Layer 2 and Layer 3 (bridged and routed) connectivity, virtualmachine hypervisors, Layer 4 to Layer 7 devices, and so on. If a tenantrequires interface configurations other than those provided in thedefault link, Cisco Discovery Protocol (CDP), Link Layer DiscoveryProtocol (LLDP), Link Aggregation Control Protocol (LACP), or SpanningTree Protocol (STP), LISP (Locator Identity Separation Protocol) anadministrator can configure access policies to enable suchconfigurations on the access ports of Leafs 104.

Node 206 can contain fabric policies that govern the operation of theswitch fabric ports, including such functions as Network Time Protocol(NTP) server synchronization, Intermediate System-to-Intermediate SystemProtocol (IS-IS), Border Gateway Protocol (BGP) route reflectors, DomainName System (DNS) and so on. The fabric MO contains objects such aspower supplies, fans, chassis, and so on.

Node 208

Node 208 can contain VM domains that group VM controllers with similarnetworking policy requirements. VM controllers can share virtual space(e.g., VLAN or VXLAN space) and application EPGs. Controllers 116communicate with the VM controller to publish network configurationssuch as port groups that are then applied to the virtual workloads.

Node 210

Node 210 can contain Layer 4 to Layer 7 service integration life cycleautomation framework that enables the system to dynamically respond whena service comes online or goes offline. Policies can provide servicedevice package and inventory management functions.

Node 212

Node 212 can contain access, authentication, and accounting (AAA)policies that govern user privileges, roles, and security domains ofFabric 120.

The hierarchical policy model can fit well with an API, such as a RESTAPI interface. When invoked, the API can read from or write to objectsin the MIT. URLs can map directly into distinguished names that identifyobjects in the MIT. Data in the MIT can be described as a self-containedstructured tree text document encoded in XML or JSON, for example.

FIG. 2B illustrates an example object model 220 for a tenant portion ofMIM 200. As previously noted, a tenant is a logical container forapplication policies that enable an administrator to exercisedomain-based access control. A tenant thus represents a unit ofisolation from a policy perspective, but it does not necessarilyrepresent a private network. Tenants can represent a customer in aservice provider setting, an organization or domain in an enterprisesetting, or just a convenient grouping of policies. Moreover, tenantscan be isolated from one another or can share resources.

Tenant portion 204A of MIM 200 can include various entities, and theentities in Tenant Portion 204A can inherit policies from parententities. Non-limiting examples of entities in Tenant Portion 204A caninclude Filters 240, Contracts 236, Outside Networks 222, Bridge Domains230, VRF Instances 234, and Application Profiles 224.

Bridge Domains 230 can include Subnets 232. Contracts 236 can includeSubjects 238. Application Profiles 224 can contain one or more EPGs 226.Some applications can contain multiple components. For example, ane-commerce application could require a web server, a database server,data located in a storage area network, and access to outside resourcesthat enable financial transactions. Application Profile 224 contains asmany (or as few) EPGs as necessary that are logically related toproviding the capabilities of an application.

EPG 226 can be organized in various ways, such as based on theapplication they provide, the function they provide (such asinfrastructure), where they are in the structure of the data center(such as DMZ), or whatever organizing principle that a fabric or tenantadministrator chooses to use.

EPGs in the fabric can contain various types of EPGs, such asapplication EPGs, Layer 2 external outside network instance EPGs, Layer3 external outside network instance EPGs, management EPGs forout-of-band or in-band access, etc. EPGs 226 can also contain Attributes228, such as encapsulation-based EPGs, IP-based EPGs, or MAC-based EPGs.

As previously mentioned, EPGs can contain endpoints (e.g., EPs 122) thathave common characteristics or attributes, such as common policyrequirements (e.g., security, virtual machine mobility (VMM), QoS, orLayer 4 to Layer 7 services). Rather than configure and manage endpointsindividually, they can be placed in an EPG and managed as a group.

Policies apply to EPGs, including the endpoints they contain. An EPG canbe statically configured by an administrator in Controllers 116, ordynamically configured by an automated system such as VCENTER orOPENSTACK.

To activate tenant policies in Tenant Portion 204A, fabric accesspolicies should be configured and associated with tenant policies.Access policies enable an administrator to configure other networkconfigurations, such as port channels and virtual port channels,protocols such as LLDP, CDP, or LACP, and features such as monitoring ordiagnostics.

FIG. 2C illustrates an example Association 260 of tenant entities andaccess entities in MIM 200. Policy Universe 202 contains Tenant Portion204A and Access Portion 206A. Thus, Tenant Portion 204A and AccessPortion 206A are associated through Policy Universe 202.

Access Portion 206A can contain fabric and infrastructure accesspolicies. Typically, in a policy model, EPGs are coupled with VLANs. Fortraffic to flow, an EPG is deployed on a leaf port with a VLAN in aphysical, VMM, L2 out, L3 out, or Fiber Channel domain, for example.

Access Portion 206A thus contains Domain Profile 236 which can define aphysical, VMM, L2 out, L3 out, or Fiber Channel domain, for example, tobe associated to the EPGs. Domain Profile 236 contains VLAN InstanceProfile 238 (e.g., VLAN pool) and Attacheable Access Entity Profile(AEP) 240, which are associated directly with application EPGs. The AEP240 deploys the associated application EPGs to the ports to which it isattached, and automates the task of assigning VLANs. While a large datacenter can have thousands of active VMs provisioned on hundreds ofVLANs, Fabric 120 can automatically assign VLAN IDs from VLAN pools.This saves time compared with trunking down VLANs in a traditional datacenter.

FIG. 2D illustrates a schematic diagram of example models forimplementing MIM 200. The network assurance models can include L_Model270A (Logical Model), LR_Model 270B (Logical Rendered Model or LogicalRuntime Model), Li_Model 272 (Logical Model for i), Ci_Model 274(Concrete model for i), and Hi_Model 276 (Hardware model or TCAM Modelfor i).

L_Model 270A is the logical representation of the objects and theirrelationships in MIM 200. L_Model 270A can be generated by Controllers116 based on configurations entered in Controllers 116 for the network,and thus represents the configurations of the network at Controllers116. This is the declaration of the “end-state” expression that isdesired when the elements of the network entities (e.g., applications)are connected and Fabric 120 is provisioned by Controllers 116. In otherwords, because L_Model 270A represents the configurations entered inControllers 116, including the objects and relationships in MIM 200, itcan also reflect the “intent” of the administrator: how theadministrator wants the network and network elements to behave.

LR_Model 270B is the abstract model expression that Controllers 116(e.g., APICs in ACI) resolve from L_Model 270A. LR_Model 270B can thusprovide the elemental configuration components that would be deliveredto the physical infrastructure (e.g., Fabric 120) to execute one or morepolicies. For example, LR_Model 270B can be delivered to Leafs 104 inFabric 120 to configure Leafs 104 for communication with attachedEndpoints 122.

Li_Model 272 is a switch-level or switch-specific model obtained fromLogical Model 270A and/or Resolved Model 270B. For example, Li_Model 272can represent the portion of L_Model 270A and/or LR Model 270Bpertaining to a specific switch or router i. To illustrate, Li_Model 272Li can represent the portion of L_Model 270A and/or LR_Model 270Bpertaining to Leaf 1 (104). Thus, Li_Model 272 can be generated fromL_Model 270A and/or LR_Model 270B for one or more switch or routers(e.g., Leafs 104 and/or Spines 102) on Fabric 120.

Ci_Model 274 is the actual in-state configuration at the individualfabric member i (e.g., switch i). In other words, Ci_Model 274 is aswitch-level or switch-specific model that is based on Li_Model 272. Forexample, Controllers 116 can deliver Li_Model 272 to Leaf 1 (104). Leaf1 (104) can take Li_Model 272, which can be specific to Leaf 1 (104),and render the policies in Li_Model 272 into a concrete model, Ci_Model274, that runs on Leaf 1 (104). Leaf 1 (104) can render Li_Model 272 viathe OS on Leaf 1 (104), for example. Thus, Ci_Model 274 can be analogousto compiled software, as it is the form of Li_Model 272 that the switchOS at Leaf 1 (104) can execute.

Hi_Model 276 is also a switch-level or switch-specific model for switchi, but is based on Ci_Model 274 for switch i. Hi_Model 276 is the actualconfiguration (e.g., rules) stored or rendered on the hardware or memory(e.g., TCAM memory) at the individual fabric member i (e.g., switch i).For example, Hi_Model 276 can represent the configurations (e.g., rules)which Leaf 1 (104) stores or renders on the hardware (e.g., TCAM memory)of Leaf 1 (104) based on Ci_Model 274 at Leaf 1 (104). The switch OS atLeaf 1 (104) can render or execute Ci_Model 274, and Leaf 1 (104) canstore or render the configurations from Ci Model in storage, such as thememory or TCAM at Leaf 1 (104). The configurations from Hi_Model 276stored or rendered by Leaf 1 (104) represent the configurations thatwill be implemented by Leaf 1 (104) when processing traffic.

While Models 272, 274, 276 are shown as device-specific models, similarmodels can be generated or aggregated for a collection of fabric members(e.g., Leafs 104 and/or Spines 102) in Fabric 120. When combined,device-specific models, such as Model 272, Model 274, and/or Model 276,can provide a representation of Fabric 120 that extends beyond aparticular device. For example, in some cases, Li_Model 272, Ci Model272, and/or Hi Model 272 associated with some or all individual fabricmembers (e.g., Leafs 104 and Spines 102) can be combined or aggregatedto generate one or more aggregated models based on the individual fabricmembers.

As referenced herein, the terms H Model, T Model, and TCAM Model can beused interchangeably to refer to a hardware model, such as Hi_Model 276.For example, Ti Model, Hi Model and TCAMi Model may be usedinterchangeably to refer to Hi_Model 276.

Models 270A, 270B, 272, 274, 276 can provide representations of variousaspects of the network or various configuration stages for MIM 200. Forexample, one or more of Models 270A, 270B, 272, 274, 276 can be used togenerate Underlay Model 278 representing one or more aspects of Fabric120 (e.g., underlay topology, routing, etc.), Overlay Model 280representing one or more aspects of the overlay or logical segment(s) ofNetwork Environment 100 (e.g., COOP, MPBGP, tenants, VRFs, VLANs,VXLANs, virtual applications, VMs, hypervisors, virtual switching,etc.), Tenant Model 282 representing one or more aspects of Tenantportion 204A in MIM 200 (e.g., security, forwarding, service chaining,QoS, VRFs, BDs, Contracts, Filters, EPGs, subnets, etc.), ResourcesModel 284 representing one or more resources in Network Environment 100(e.g., storage, computing, VMs, port channels, physical elements, etc.),etc.

In general, L_Model 270A can be the high-level expression of what existsin the LR_Model 270B, which should be present on the concrete devices asCi_Model 274 and Hi_Model 276 expression. If there is any gap betweenthe models, there may be inconsistent configurations or problems.

FIG. 3A illustrates a diagram of an example Assurance Appliance 300 fornetwork assurance. In this example, Assurance Appliance 300 can includek VMs 110 operating in cluster mode. VMs are used in this example forexplanation purposes. However, it should be understood that otherconfigurations are also contemplated herein, such as use of containers,bare metal devices, Endpoints 122, or any other physical or logicalsystems. Moreover, while FIG. 3A illustrates a cluster modeconfiguration, other configurations are also contemplated herein, suchas a single mode configuration (e.g., single VM, container, or server)or a service chain for example.

Assurance Appliance 300 can run on one or more Servers 106, VMs 110,Hypervisors 108, EPs 122, Leafs 104, Controllers 116, or any othersystem or resource. For example, Assurance Appliance 300 can be alogical service or application running on one or more VMs 110 in NetworkEnvironment 100.

The Assurance Appliance 300 can include Data Framework 308, which can bebased on, for example, APACHE APEX and HADOOP. In some cases, assurancechecks can be written as individual operators that reside in DataFramework 308. This enables a natively horizontal scale- outarchitecture that can scale to arbitrary number of switches in Fabric120 (e.g., ACI fabric).

Assurance Appliance 300 can poll Fabric 120 at a configurableperiodicity (e.g., an epoch). The analysis workflow can be setup as aDAG (Directed Acyclic Graph) of Operators 310, where data flows from oneoperator to another and eventually results are generated and persistedto Database 302 for each interval (e.g., each epoch).

The north-tier implements API Server (e.g., APACHE Tomcat and Springframework) 304 and Web Server 306. A graphical user interface (GUI)interacts via the APIs exposed to the customer. These APIs can also beused by the customer to collect data from Assurance Appliance 300 forfurther integration into other tools.

Operators 310 in Data Framework 308 (e.g., APEX/Hadoop) can togethersupport assurance operations. Below are non-limiting examples ofassurance operations that can be performed by Assurance Appliance 300via Operators 310.

Security Policy Adherence

Assurance Appliance 300 can check to make sure the configurations orspecification from L_Model 270A, which may reflect the user's intent forthe network, including for example the security policies andcustomer-configured contracts, are correctly implemented and/or renderedin Li_Model 272, Ci_Model 274, and Hi_Model 276, and thus properlyimplemented and rendered by the fabric members (e.g., Leafs 104), andreport any errors, contract violations, or irregularities found.

Static Policy Analysis

Assurance Appliance 300 can check for issues in the specification of theuser's intent or intents (e.g., identify contradictory or conflictingpolicies in L_Model 270A).

TCAM Utilization

TCAM is a scarce resource in the fabric (e.g., Fabric 120). However,Assurance Appliance 300 can analyze the TCAM utilization by the networkdata (e.g., Longest Prefix Match (LPM) tables, routing tables, VLANtables, BGP updates, etc.), Contracts, Logical Groups 118 (e.g., EPGs),Tenants, Spines 102, Leafs 104, and other dimensions in NetworkEnvironment 100 and/or objects in MIM 200, to provide a network operatoror user visibility into the utilization of this scarce resource. Thiscan greatly help for planning and other optimization purposes.

Endpoint Checks

Assurance Appliance 300 can validate that the fabric (e.g. fabric 120)has no inconsistencies in the Endpoint information registered (e.g., twoleafs announcing the same endpoint, duplicate subnets, etc.), amongother such checks.

Tenant Routing Checks

Assurance Appliance 300 can validate that BDs, VRFs, subnets (bothinternal and external), VLANs, contracts, filters, applications, EPGs,etc., are correctly programmed.

Infrastructure Routing

Assurance Appliance 300 can validate that infrastructure routing (e.g.,IS-IS protocol) has no convergence issues leading to black holes, loops,flaps, and other problems.

MP-BGP Route Reflection Checks

The network fabric (e.g., Fabric 120) can interface with other externalnetworks and provide connectivity to them via one or more protocols,such as Border Gateway Protocol (BGP), Open Shortest Path First (OSPF),etc. The learned routes are advertised within the network fabric via,for example, MP-BGP. These checks can ensure that a route reflectionservice via, for example, MP-BGP (e.g., from Border Leaf) does not havehealth issues.

Logical Lint and Real-time Change Analysis

Assurance Appliance 300 can validate rules in the specification of thenetwork (e.g., L_Model 270A) are complete and do not haveinconsistencies or other problems. MOs in the MIM 200 can be checked byAssurance Appliance 300 through syntactic and semantic checks performedon L_Model 270A and/or the associated configurations of the MOs in MIM200. Assurance Appliance 300 can also verify that unnecessary, stale,unused or redundant configurations, such as contracts, are removed.

FIG. 3B illustrates an architectural diagram of an example system 350for network assurance. In some cases, system 350 can correspond to theDAG of Operators 310 previously discussed with respect to FIG. 3A Inthis example, Topology Explorer 312 communicates with Controllers 116(e.g., APIC controllers) in order to discover or otherwise construct acomprehensive topological view of Fabric 120 (e.g., Spines 102, Leafs104, Controllers 116, Endpoints 122, and any other components as well astheir interconnections). While various architectural components arerepresented in a singular, boxed fashion, it is understood that a givenarchitectural component, such as Topology Explorer 312, can correspondto one or more individual Operators 310 and may include one or morenodes or endpoints, such as one or more servers, VMs, containers,applications, service functions (e.g., functions in a service chain orvirtualized network function), etc.

Topology Explorer 312 is configured to discover nodes in Fabric 120,such as Controllers 116, Leafs 104, Spines 102, etc. Topology Explorer312 can additionally detect a majority election performed amongstControllers 116, and determine whether a quorum exists amongstControllers 116. If no quorum or majority exists, Topology Explorer 312can trigger an event and alert a user that a configuration or othererror exists amongst Controllers 116 that is preventing a quorum ormajority from being reached. Topology Explorer 312 can detect Leafs 104and Spines 102 that are part of Fabric 120 and publish theircorresponding out-of-band management network addresses (e.g., IPaddresses) to downstream services. This can be part of the topologicalview that is published to the downstream services at the conclusion ofTopology Explorer's 312 discovery epoch (e.g., 5 minutes, or some otherspecified interval).

Unified Collector 314 can receive the topological view from TopologyExplorer 312 and use the topology information to collect information fornetwork assurance from Fabric 120. Such information can include L_Model270A and/or LR_Model 270B from Controllers 116, switch softwareconfigurations (e.g., Ci_Model 274) from Leafs 104 and/or Spines 102,hardware configurations (e.g., Hi_Model 276) from Leafs 104 and/orSpines 102, etc. Unified Collector 314 can collect Ci_Model 274 andHi_Model 276 from individual fabric members (e.g., Leafs 104 and Spines102).

Unified Collector 314 can poll the devices that Topology Explorer 312discovers in order to collect data from Fabric 120 (e.g., from theconstituent members of the fabric).Unified Collector 314 can collect thedata using interfaces exposed by Controller 116 and/or switch software(e.g., switch OS), including, for example, a Representation StateTransfer (REST) Interface and a Secure Shell (SSH) Interface.

In some cases, Unified Collector 314 collects L_Model 270A, LR_Model270B, and/or Ci Model 274 via a REST API, and the hardware information(e.g., configurations, tables, fabric card information, rules, routes,etc.) via SSH using utilities provided by the switch software, such asvirtual shell (VSH or VSHELL) for accessing the switch command-lineinterface (CLI) or VSH_LC shell for accessing runtime state of the linecard.

Unified Collector 314 can poll other information from Controllers 116,including: topology information, tenant forwarding/routing information,tenant security policies, contracts, interface policies, physical domainor VMM domain information, 00B (out-of-band) management IP's of nodes inthe fabric, etc.

Unified Collector 314 can also poll other information from Leafs 104 andSpines 102, such as: Ci Models 274 for VLANs, BDs, security policies,Link Layer Discovery Protocol (LLDP) connectivity information of Leafs104 and/or Spines 102, endpoint information from EPM/COOP, fabric cardinformation from Spines 102, routing information base (RIB) tables,forwarding information base (FIB) tables from Leafs 104 and/or Spines102, security group hardware tables (e.g., TCAM tables) from switches,etc.

Assurance Appliance 300 can run one or more instances of UnifiedCollector 314. For example, Assurance Appliance 300 can run one, two,three, or more instances of Unified Collector 314. The task of datacollecting for each node in the topology (e.g., Fabric 120 includingSpines 102, Leafs 104, Controllers 116, etc.) can be sharded or loadbalanced, to a unique instance of Unified Collector 314. Data collectionacross the nodes can thus be performed in parallel by one or moreinstances of Unified Collector 314. Within a given node, commands anddata collection can be executed serially. Assurance Appliance 300 cancontrol the number of threads used by each instance of Unified Collector314 to poll data from Fabric 120.

Data collected by Unified Collector 314 can be compressed and sent todownstream services. In some examples, Unified Collector 314 can collectdata in an online fashion or real- time fashion, and send the datadownstream, as it is collected, for further analysis. In some examples,Unified Collector 314 can collect data in an offline fashion, andcompile the data for later analysis or transmission.

Assurance Appliance 300 can contact Controllers 116, Spines 102, Leafs104, and other nodes to collect various types of data. In somescenarios, Assurance Appliance 300 may experience a failure (e.g.,connectivity problem, hardware or software error, etc.) that prevents itfrom being able to collect data for a period of time. AssuranceAppliance 300 can handle such failures seamlessly, and generate eventsbased on such failures.

Switch Logical Policy Generator 316 can receive L_Model 270A and/or LRModel 270B from Unified Collector 314 and calculate Li_Model 272 foreach network device i (e.g., switch i) in Fabric 120. For example,Switch Logical Policy Generator 316 can receive L_Model 270A and/orLR_Model 270B and generate Li_Model 272 by projecting a logical modelfor each individual node i (e.g., Spines 102 and/or Leafs 104) in Fabric120. Switch Logical Policy Generator 316 can generate Li_Model 272 foreach switch in Fabric 120, thus creating a switch logical model based onL_Model 270A for each switch.

Switch Logical Configuration Generator 316 can also perform changeanalysis and generate lint events or records for problems discovered inL_Model 270A and/or LR Model 270B. The lint events or records can beused to generate alerts for a user or network operator.

Policy Operator 318 can receive Ci_Model 274 and Hi_Model 276 for eachswitch from Unified Collector 314, and Li_Model 272 for each switch fromSwitch Logical Policy Generator 316, and perform assurance checks andanalysis (e.g., security adherence checks, TCAM utilization analysis,etc.) based on Ci_Model 274, Hi_Model 276, and Li_Model 272. PolicyOperator 318 can perform assurance checks on a switch-by-switch basis bycomparing one or more of the models.

Returning to Unified Collector 314, Unified Collector 314 can also sendL_Model 270A and/or LR_Model 270B to Routing Policy Parser 320, andCi_Model 274 and Hi_Model 276 to Routing Parser 326.

Routing Policy Parser 320 can receive L_Model 270A and/or LR_Model 270Band parse the model(s) for information that may be relevant todownstream operators, such as Endpoint Checker 322 and Tenant RoutingChecker 324. Similarly, Routing Parser 326 can receive Ci_Model 274 andHi_Model 276 and parse each model for information for downstreamoperators, Endpoint Checker 322 and Tenant Routing Checker 324.

After Ci_Model 274, Hi_Model 276, L_Model 270A and/or LR_Model 270B areparsed, Routing Policy Parser 320 and/or Routing Parser 326 can sendcleaned-up protocol buffers (Proto Buffs) to the downstream operators,Endpoint Checker 322 and Tenant Routing Checker 324. Endpoint Checker322 can then generate events related to Endpoint violations, such asduplicate IPs, APIPA, etc., and Tenant Routing Checker 324 can generateevents related to the deployment of BDs, VRFs, subnets, routing tableprefixes, etc.

FIG. 3C illustrates a schematic diagram of an example system for staticpolicy analysis in a network (e.g., Network Environment 100). StaticPolicy Analyzer 360 can perform assurance checks to detect configurationviolations, logical lint events, contradictory or conflicting policies,unused contracts, incomplete configurations, etc. Static Policy Analyzer360 can check the specification of the user's intent or intents inL_Model 270A to determine if any configurations in Controllers 116 areinconsistent with the specification of the user's intent or intents.

Static Policy Analyzer 360 can include one or more of the Operators 310executed or hosted in Assurance Appliance 300. However, in otherconfigurations, Static Policy Analyzer 360 can run one or more operatorsor engines that are separate from Operators 310 and/or AssuranceAppliance 300. For example, Static Policy Analyzer 360 can be a VM, acluster of VMs, or a collection of endpoints in a service functionchain.

Static Policy Analyzer 360 can receive as input L_Model 270A fromLogical Model Collection Process 366 and Rules 368 defined for eachfeature (e.g., object) in L_Model 270A. Rules 368 can be based onobjects, relationships, definitions, configurations, and any otherfeatures in MIM 200. Rules 368 can specify conditions, relationships,parameters, and/or any other information for identifying configurationviolations or issues.

Moreover, Rules 368 can include information for identifying syntacticviolations or issues. For example, Rules 368 can include one or morerules for performing syntactic checks. Syntactic checks can verify thatthe configuration of L_Model 270A is complete, and can help identifyconfigurations or rules that are not being used. Syntactic checks canalso verify that the configurations in the hierarchical MIM 200 arecomplete (have been defined) and identify any configurations that aredefined but not used. To illustrate, Rules 368 can specify that everytenant in L_Model 270A should have a context configured; every contractin L_Model 270A should specify a provider EPG and a consumer EPG; everycontract in L_Model 270A should specify a subject, filter, and/or port;etc.

Rules 368 can also include rules for performing semantic checks andidentifying semantic violations or issues. Semantic checks can checkconflicting rules or configurations. For example, Rulel and Rule2 canhave aliasing issues, Rulel can be more specific than Rule2 and therebycreate conflicts/issues, etc. Rules 368 can define conditions which mayresult in aliased rules, conflicting rules, etc. To illustrate, Rules368 can specify that an allow policy for a specific communicationbetween two objects can conflict with a deny policy for the samecommunication between two objects if they allow policy has a higherpriority than the deny policy, or a rule for an object renders anotherrule unnecessary.

Static Policy Analyzer 360 can apply Rules 368 to L_Model 270A to checkconfigurations in L_Model 270A and output Configuration Violation Events370 (e.g., alerts, logs, notifications, etc.) based on any issuesdetected. Configuration Violation Events 370 can include semantic orsemantic problems, such as incomplete configurations, conflictingconfigurations, aliased rules, unused configurations, errors, policyviolations, misconfigured objects, incomplete configurations, incorrectcontract scopes, improper object relationships, etc.

In some cases, Static Policy Analyzer 360 can iteratively traverse eachnode in a tree generated based on L_Model 270A and/or MIM 200, and applyRules 368 at each node in the tree to determine if any nodes yield aviolation (e.g., incomplete configuration, improper configuration,unused configuration, etc.). Static Policy Analyzer 360 can outputConfiguration Violation Events 370 when it detects any violations.

FIG. 4 illustrates a flowchart for an example network assurance method.The method shown in FIG. 4 is provided by way of example, as there are avariety of ways to carry out the method. Additionally, while the examplemethod is illustrated with a particular order of blocks, those ofordinary skill in the art will appreciate that FIG. 4 and the blocksshown therein can be executed in any order and can include fewer or moreblocks than illustrated.

Each block shown in FIG. 4 represents one or more steps, processes,methods or routines in the method. For the sake of clarity andexplanation purposes, the blocks in FIG. 4 are described with referenceto Assurance Appliance 300, Models 270A-B, 272, 274, 276, and NetworkEnvironment 100, as shown in FIGS. 1A-B, 2D, and 3A.

At step 400, Assurance Appliance 300 can collect data and obtain modelsassociated with Network Environment 100. The models can include Models270A-B, 272, 274, 276. The data can include fabric data (e.g., topology,switch, interface policies, application policies, EPGs, etc.), networkconfigurations (e.g., BDs, VRFs, L2 Outs, L3 Outs, protocolconfigurations, etc.), security configurations (e.g., contracts,filters, etc.), service chaining configurations, routing configurations,and so forth. Other information collected or obtained can include, forexample, network data (e.g., RIB/FIB, VLAN, MAC, ISIS, DB, BGP, OSPF,ARP, VPC, LLDP, MTU, QoS, etc.), rules and tables (e.g., TCAM rules,ECMP tables, etc.), endpoint dynamics (e.g., EPM, COOP EP DB, etc.),statistics (e.g., TCAM rule hits, interface counters, bandwidth, etc.).

At step 402, Assurance Appliance 300 can analyze and model the receiveddata and models. For example, Assurance Appliance 300 can perform formalmodeling and analysis, which can involve determining equivalency betweenmodels, including configurations, policies, etc.

At step 404, Assurance Appliance 300 can generate one or more smartevents. Assurance Appliance 300 can generate smart events using deepobject hierarchy for detailed analysis, such as Tenants, switches, VRFs,rules, filters, routes, prefixes, ports, contracts, subjects, etc.

At step 406, Assurance Appliance 300 can visualize the smart events,analysis and/or models. Assurance Appliance 300 can display problems andalerts for analysis and debugging, in a user-friendly GUI.

In a network environment, sensors can be implemented at various devicesor elements in the network to collect data from different locations. Inparticular the sensors can be implemented, as discussed previously,through APIs provided by network elements and used to query the networkelements for the data. The collected data from the sensors can beanalyzed to monitor and troubleshoot the network. The data collectedfrom the sensors can provide valuable details about the status,security, or performance of the network, as well as any networkelements. Information about the sensors can also help interpret the datafrom the sensors, in order to infer or ascertain additional details fromthe collected data. For example, collected data can be used to modelbehavior and configurations within a network fabric in order to assurethe network.

SDNs have been developed in order to improve performance in networks andprovide greater control in managing networks. SDNs can decouple networkcontrol and forwarding functions to create programmable network control.In turn, this can abstract an underlying network infrastructure fromapplications and network services. This can allow for easy control andconfiguring of network environments by network administrators.

Sensors and network tools can be utilized in SDNs to provide assurancein the SDNs. Specifically, sensors can be implemented in an SDN togather data for the SDN and network tools can model operation andbehaviors of the SDN based on data gathered by the sensors.Subsequently, events can be generated for the SDN using the gathereddata and models in order to provide assurance in the SDN. Such sensorsand network tools can provide assurance on a per-SDN basis.Specifically, such sensors and network tools can provide assurance for anetwork in a specific network domain without respect to communicationsand interactions between the network and other networks in differentnetwork domains. This is problematic, as network traffic often timesoriginates from one network domain and extends into another network in adifferent network domain. However, as assurance is only provided on aper-network domain basis, the network traffic is only assured withrespect to a specific network that a portion of the network trafficpasses through. More specifically, the network traffic is not assuredacross multiple networks, e.g. SDNs, in multiple domains that thenetwork traffic ultimately spans across. There therefore exist needs forproviding assurance across multiple networks, e.g. SDNs, in differentnetwork domains.

In order to address these challenges, fabric data for a first network ina network domain can be normalized based on a different network domainof a second network. As discussed previously, fabric data can includeapplicable data indicating operations of a network fabric to providenetwork service access (e.g., topology, switch, interface policies,application policies, EPGs, etc.). Subsequently, normalized fabric dataof the first network can be correlated with fabric data of the secondnetwork to create correlated fabric data across the networks. Thecorrelated fabric data can then be used to provide assurance across thenetworks in the different domains.

Further, in typical SDNs, policies can be configured which ultimatelydeploys rules in switches to enforce control on underlying traffic. Inparticular, policies can be deployed to controllers and/or identityservices engines where the policies can be used to deploy rules forcontrolling underlying traffic. As part of providing assurance, suchpolicies can be modeled to ensure that desired, e.g. intent-based,traffic control is actually being enforced in the SDN. As assurance isprovided on a per-network basis, policy checks are performedirrespective of policies that exist in networks in different domains.Specifically, SDNs in different network domains that are communicatingwith each other can have conflicting or otherwise incompatible policies.In turn, providing assurance on a per-network basis can lead to failuresin recognizing the conflicts between the policies. There therefore existneeds for providing policy assurance across multiple networks, e.g.SDNs, in different network domains.

In order to address these challenges fabric data indicating policies ina first network and fabric data indicating policies in a second networkcan be collected. The first network and the second network can be indifferent network domains. The policies in the second network can thenbe normalized with respect to the policies in the first network based onthe network domain of the first network. Subsequently, the normalizedpolicies of the second network can be correlated with the policies ofthe first network to identify whether the policies are compatible. Inturn, policy assurance can be provided across the first network and thesecond network, and corresponding different network domains, based onwhether the policies are found to be compatible with each other.

FIG. 5 illustrates an environment 500 for providing cross-domainassurance. The example environment shown in FIG. 5 includes a firstnetwork 502, a second network 504, and a cross-domain assurance system506. The first network 502 and the second network 504 function toprovide network service access. Specifically, the first network 502 andthe second network 504 can be formed according to an applicable networkenvironment for providing network service access, such as the networkenvironments 100 shown in FIGS. 1A and 1B. In providing network serviceaccess the first network 502 and the second network 504 can communicatewith each other. Specifically, a traffic flow can originate at or passthrough the second network 504 into the first network 502 and viceversa. Further, in communicating with each other, traffic flows passingbetween the first network 502 and the second network 504 can passthrough a firewall.

Both the first network 502 and the second network 504 are in orotherwise form separate network domains. More specifically, both thefirst network 502 and the second network 504 can be separate SDNnetworks in separate network domains. The corresponding network domainsof the first network 502 and the second network 504 can be differenttypes of network domains. For example, the first network 502 can be anACI network at a datacenter and the second network 504 can be a campusor digital network architecture (DNA) network of an enterprise. Furtherin the example, the first network 502 can implement policies within thenetwork through one or more APICs while the second network 504 canimplement policies within the network through one or more DNAcontrollers, e.g. in a DNA center (DNAC), and/or an identity servicesengine. In another example, the first network 502 can be a DNA networkof an enterprise and the second network 504 can be an ACI network at adatacenter. Alternatively, the corresponding network domains of thefirst network 502 and the second network 504 can be the same type ofnetwork domains. For example, both the first network 502 and the secondnetwork 504 can be either ACI networks or DNA networks of one or moreenterprises.

The cross-domain assurance system 506 functions to provide cross-domainassurance for different networks. More specifically, the cross-domainassurance system 506 functions to provide assurance across thecorresponding network domains of the first network 502 and the secondnetwork 504. In providing assurance across multiple network domains, thecross-domain assurance system 506 can assure networks based oncommunications between different networks and corresponding differentnetwork domains. More specifically, in providing assurance acrossmultiple network domains, the cross-domain assurance system 506 canassure network communications that occur between different networks indifferent domains, and potentially with different domain types. This isadvantageous as assuring networks across different domains can ensuredifferent networks are actually compatible with each other to providenetwork service access across the different networks. For example, aswill be discussed in greater detail later, the cross-domain assurancesystem 506 can ensure that policies in different networks are compatiblewith each other for controlling underlying traffic transmitted withinand between the different networks.

In providing cross-domain assurance for different networks, thecross-domain assurance system 506 can collect fabric data from thenetworks. In particular, the cross-domain assurance system 506 cancollect fabric data from the first network 502 and fabric data from thesecond network 504. In collecting fabric data from networks, thecross-domain assurance system 506 can functions as, or otherwise beimplemented as, part of an applicable appliance for providing assurancein a network environment, such as the example assurance appliance 300discussed herein. The cross-domain assurance system 506 can collectfabric data from an applicable source in a network environment. Forexample, if a network is an ACI network, then the cross-domain assurancesystem 506 can collect fabric data, including policies implemented inthe ACI network, from one or more APICs in the framework of the ACInetwork. In another example, if a network is a DNA network, then thecross-domain assurance system 506 can collect fabric data, includingpolicies implemented in the DNA network, from either or both DNAcontrollers of the DNA network and an identity services engine for theDNA network.

The cross-domain assurance system 506 can normalize fabric datacollected from a network with respect to fabric data collected fromanother network, as part of providing cross- domain assurance across thenetworks. Specifically, the cross-domain assurance system 506 cannormalize fabric data collected from the second network 504 with respectto fabric data collected from the first network 502. Alternatively, thecross-domain assurance system 506 can normalize fabric data collectedfrom the first network 502 with respect to fabric data collected fromthe second network 504. The cross-domain assurance system 506 cannormalize fabric data of a first network based on a network domain of adifferent network. Specifically, the cross-domain assurance system 506can translate fabric data into a form that is compatible with fabricdata gathered from another network, e.g. for purposes of correlating thenetwork data across networks. For example, as will be discussed ingreater detail later with respect to network policies, the cross- domainassurance system 506 can translate policies implemented in the secondnetwork 104 into a form of policies that can be understood with orotherwise correlated with policies implemented in the first network 102.

Further, the cross-domain assurance system 506 can correlate fabric dataof one network with normalized fabric data of another network forpurposes of providing assurance across the networks. Specifically, thecross-domain assurance system 506 can correlate normalized fabric dataof the second network 504 with fabric data of the first network 502 forpurposes of providing assurance across the networks. Alternatively, thecross-domain assurance system 506 can correlated normalized fabric dataof the first network 502 with fabric data of the second network 504 forpurposes of providing assurance across the networks. For example, thecross-domain assurance system 506 can correlate fabric data acrossnetworks to determine whether the network are correctly configured totransmit data between each other, e.g. as part of providing assuranceacross the networks. More specifically, in correlating fabric dataacross the networks, as will be discussed in greater detail later withrespect to policies, the cross-domain assurance system 506 can use thecorrelated fabric data to provide assurance that the networks canoperate together to provide network services according to administratorintent.

The cross-domain assurance system 506 can provide assurance acrossnetwork domains based on policies implemented in the network domains.Specifically, policies can be implemented in networks and otherwisedefined based on network types of networks in which the policies areimplemented. Subsequently, the cross-domain assurance system 506 canprovide assurance across networks based on policies specific to thenetworks. For example, a policy can be defined based on communicationswith one or more endpoints and endpoint groups in an ACI network.Subsequently, the cross-domain assurance system 506 can provideassurance across network domains based on the one or more endpoints andendpoint groups in the ACI network. In another example, a policy can bedefined based on communications with one or more security groups in aDNA network. More specifically, fabric data for a DNA network caninclude policies and associated security groups, e.g. security grouptags, associated with the policies. Security groups can include one ormore users and be formed based on user types and/or device types. Forexample, security group can be defined to include faculty members in acampus network set up as a DNA network. Subsequently, the cross-domainassurance system 506 can provide assurance across network domains basedon the one or more security groups in the DNA network. Specifically, thecross-domain assurance system 506 can guarantee or determine thatnetworks are behaving as intended from a perspective across thenetworks, as part of providing cross-domain assurance. For example, thecross-domain assurance system 506 can ensure that operating in acompatible fashion with each other according to intent of a networkadministrator.

By providing assurance across network domains based on policiesimplemented in the network domains, the cross-domain assurance system506 can ensure that networks are compatible for communicating with eachother to provide network service access. Specifically, by providingassurance across network domains based on policies implemented in thenetwork domains, the cross-domain assurance system 506 can ensure thatnetworks have compatible policies to control underlying traffic flowwithin and between the networks. For example, if a faculty member in aDNA network is allowed access to a grading portal in an ACI network,then the cross-domain assurance system 506 can determine that thepolicies in the DNA network and the ACI network are compatible toactually allow the faculty member to access the grading portal throughthe DNA network and the ACI network. Further, the ACI network canactually control access by the faculty member to the grading portal,e.g. by enforcing policies in the ACI network, based on a correspondingIP address of the faculty member which is used by the ACI network toidentify the faculty member as a source of traffic.

The cross-domain assurance system 506 can normalize collected fabricdata of policies for purposes of assuring policies across multiplenetwork domains. Specifically, the cross-domain assurance system 506 cannormalize policies of a first network in a first domain based on asecond network in a second domain in order to provide cross-domainassurance between the first and second networks. More specifically, thecross-domain assurance system 506 can normalize policies in the secondnetwork 504 into policies for the first network 502 based on a domain ofthe first network 502. Conversely, the cross-domain assurance system 506can normalize policies in the first network 502 into policies for thesecond network 504 based on a domain of the second network 504.

Specifically, if the first network 502 is an ACI network and the secondnetwork 504 is a DNA network, then the cross-domain assurance system 506can translate policies defined for security groups in the second network504 into policies defined by endpoint groups in the first network 502.For example, policies for the second network 504 can specify studentsare allowed to communicate with a library portal in the first network502 but not a grading portal in the first network 502. Subsequently, thecross-domain assurance system 506 can translate the policies in thesecond network 504 into L3 out student SGT, which is an endpoint groupassociated with communications with the “student” security group outsideof the ACI fabric in the second network 504. While the example isdescribed with respect to the first network 502 being an ACI network andthe second network 504 being a DNA network, the same example can beapplied if the first network 502 is a DNA network and the second network504 is an ACI network and policies for the first network 502 arenormalized with respect to the second network 504.

Alternatively, if the first network 502 is a DNA network and the secondnetwork 504 is an ACI network, then the cross-domain assurance system506 can translate policies defined for endpoint groups in the secondnetwork 504 into policies defined by security groups in the firstnetwork 502. For example, policies for the second network 504 canspecify that a library portal is allowed to communicate with students ina student security group in the first network 502. Subsequently, thecross-domain assurance system 506 can translate the policies for thesecond network into a policy with security group “LibraryPortal EPG” forthe first network 502. While the example is described with respect tothe first network 502 being a DNA network and the second network 504being an ACI network, the same example can be applied if the firstnetwork 502 is an ACI network and the second network 504 is a DNAnetwork and policies for the first network 502 are normalized withrespect to the second network 504.

In providing assurance across networks in different network domains, thecross-domain assurance system 506 can correlate fabric data indicatingpolicies of different network to provide assurance, e.g. policyassurance, across the networks. Specifically, the cross-domain assurancesystem 506 can correlate normalized fabric data of policies for onenetwork with fabric data of policies for another network to providepolicy assurance across the networks. For example, the cross-domainassurance system 506 can correlate normalized policy fabric data for thesecond network 504 with policy fabric data for the first network 502 toprovide policy assurance across the first network 502 and the secondnetwork 504. Alternatively, the cross-domain assurance system 506 cancorrelate normalized policy fabric data for the first network 502 withpolicy fabric data for the second network 504 to provide policyassurance across the first network 502 and the second network 504.

In correlating policy fabric data to provide assurance across networks,the cross-domain assurance system 506 can identify whether policiesimplemented in the networks are compatible with each other, e.g. as partof correlated fabric data. Specifically, the cross-domain assurancesystem 506 can use normalized fabric data of the second network 504 andfabric data of the first network 502 to identify whether the policies ofthe second network 504 are equivalent to the policies in the firstnetwork 502. Alternatively, the cross-domain assurance system 506 canuse normalized fabric data of the first network 502 and fabric data ofthe second network 504 to identify whether the policies of the firstnetwork are equivalent to policies in the second network 504.

In determining whether policies are equivalent, the cross-domainassurance system 506 can determine whether the policies provide the sameconfigurable control of network traffic in corresponding networks, e.g.as part of correlated fabric data. For example, if a policy in thesecond network 504 allows a teacher security group to communicate with agrading portal, then the cross-domain assurance system 506 can determinethat policies in the first network 502 are equivalent to policies in thesecond network 504, if a policy in the first network 502 allows theteacher security group to communicate with the grading portal. Furtherin the example, the cross- domain assurance system 506 can translate thepolicy in the second network 504 of grading<allow>teacher into a policyfor the first network 502 of L3 out_teacher_SGT<allow>grading. Stillfurther, the cross-domain assurance system 506 can identify whether thecorresponding policy of teacher<allow>grading exists in the firstnetwork 502 and subsequently identify that equivalent policies existbetween the first network 502 and the second network 504 ifteacher<allow>grading exists in the first network 502.

The cross-domain assurance system 506 can determine mismatches, e.g. aspart of correlated fabric data, in policies between different networks.Specifically, the cross-domain assurance system 506 can identifymismatches between policies as part of determining whether the policiesare equivalent. For example, if a policy in the first network 502 allowsstudent access to a grading portal and a policy in the second network502 does not allow student access the grading portal, then thecross-domain assurance system 506 can determine the policies aremismatched in allowed student access.

In response to determining whether policies are equivalent, thecross-domain assurance system 506 can generate policy mismatch events,e.g. as part of providing assurance across networks. More specifically,if a policy mismatch is identified, then the cross-domain assurancesystem 506 can generate a policy mismatch event. The policy mismatchevent can be used to alert an administrator of policy mismatches acrossnetworks. Further, a policy mismatch event can include indicators ofactual mismatches between policies and subsequently be used to alert anadministrator of the actual mismatches. In turn, the administrator canreconfigure the policies based on the identified mismatches reported tothe administrator through the policy mismatch event. In response to arecognized policy mismatch, the cross-domain assurance system 506 Asystem can provide alerting to operator(s) and administrator(s) of theidentity management system and of the network domain(s) with specificcorrective actions in each domain to be taken to resolve any consistencyissues between policies. Specifically, the cross-domain assurance system506 can provide insight into exact semantics (e.g. forwarding, policy,etc..) that may be causing communication (or lack thereof) between twoor more endpoints in each domain and suggest specific steps to befollowed by the operator(s) or administrator(s) of each domain to eitherpermit or restrict traffic flow between these endpoints. Thecross-domain assurance system 506 can provide information related topolicy mismatches in simple query terms that allow a user to easilydigest this information. For example, the cross-domain assurance system506 can provide the ability for end users to consume this data usingsimple query terms like ‘Can A talk to B’ [where A is an endpoint in thedatacenter domain, B is an endpoint in a different SDN domain] withouthaving to understand, extrapolate and interpret the intricacies of eachSDN domain.

The cross-domain assurance system 506 can communicate with an identityaccess and management database, e.g. as implemented as part of anidentity services engine configured to manage a network, e.g. a DNAnetwork. Specifically, the cross-domain assurance system 506 can querythe identity access and management database to obtain mappings ofendpoint(s) to security group(s). Further, the cross-domain assurancesystem 506 can use mappings of endpoints to security groups to provide aframework to map business level intent (E.g. PCI servers should not talkto non-PCI endpoints). Specifically, the cross-domain assurance system506 can use a tagging mechanism to map business level intent by allowingfor mappings of operator or administrator defined tags to securitygroup(s) obtained from the identity management platform or aconfiguration management database (CMDB). [E.g. The assurance system maylet the operator define a tag of “PCI servers” for all endpoints in asubnet range (like 10.1.1.0/24) or all security groups with a certainname (like security group name contains “prod_web_”]

Further, in communicating with the identity and access managementdatabase, the cross-domain assurance system 506 to ensure data acrossnetworks in different domains is consistent. Specifically, thecross-domain assurance system 506 can cross-reference information fromthe identity management platform with normalized information from bothnetwork domains to determine the sanctity of this data across theidentity management system and both network domains is consistent.Subsequently, the cross-domain assurance system 506 can use the cross-referenced information to provide assurance, e.g. policy assurance,across the network domains.

FIG. 6 illustrates a flowchart for an example method of providingcross-domain assurance. The method shown in FIG. 6 is provided by way ofexample, as there are a variety of ways to carry out the method.Additionally, while the example method is illustrated with a particularorder of blocks, those of ordinary skill in the art will appreciate thatFIG. 6 and the blocks shown therein can be executed in any order and caninclude fewer or more blocks than illustrated.

Each block shown in FIG. 6 represents one or more steps, processes,methods or routines in the method. For the sake of clarity andexplanation purposes, the blocks in FIG. 6 are described with referenceto the environment 500, shown in FIG. 5.

At step 600, the cross-domain assurance system 506 collects first fabricdata for a first network in a first domain and second fabric data for asecond network in a second domain. The fabric data for either or boththe first network and the second network can be collected from acontroller in an ACI network. Alternatively, the fabric data for eitheror both the first network and the second network can be collected fromeither or both a controller and an identity services engine in a DNAnetwork.

At step 602, the cross-domain assurance system 506 normalizes the secondfabric data for the second network based on the first network domain tocreate normalized second fabric data. In particular, policies for thesecond network can be translated into policies capable of beingimplemented in the first network as part of normalizing the secondfabric data based on the first network domain. For example, if the firstnetwork is an ACI network, then policies for the second network can betranslated into policies for communicating with one or more endpointsand endpoint groups in the ACI network. In another example, if the firstnetwork is a DNA network, then policies for the second network can betranslated into policies for communicating with one or more securitygroups in the DNA network.

At step 604, the cross-domain assurance system 506 correlates the firstfabric data with the normalized second fabric data to create correlatedfabric data. In particular, the cross-domain assurance system 506 cancompare policies in the second network with policies in the firstnetwork, using the first fabric data and the normalized second fabricdata, to determine whether the policies are compatible with each other.Further, as part of correlating the first fabric data and the secondfabric data to create correlated fabric data, the cross-domain assurancesystem 506 can identify mismatches between policies in the first networkand policies in the second network.

At step 606, the cross-domain assurance system 506 provides assuranceacross the first network and the second network using the correlatedfabric data. Specifically, if the correlated fabric data indicates thatpolicies in the first network and policies in the second network areincompatible, then the cross-domain assurance system 506 can generate apolicy mismatch event. Subsequently, the policy mismatch event can beused to alert an administrator that policies in the first and secondnetwork mismatch. Additionally, the cross-domain assurance system 506can generate a policy mismatch event including actual mismatches betweenpolicies in the first network and policies in the second network as partof providing assurance across the first network and the second network.

At optional step, the cross-domain assurance system 506 can tagendpoints based on policies and subsequently provide assurance acrossthe first and second networks based on the tagged endpoints.Specifically, the cross-domain assurance system 506 can consume businesslevel intent (e.g. PCI servers should not talk to non-PCI users) andglean mappings of PCI, non-PCI endpoints from a policy, postureverification engine. Further, the cross-domain assurance system 506 cantag these endpoints based on user defined policies. Subsequently, thecross-domain assurance system 506 can perform cross-domain co-relationacross the identity management system (e.g. Cisco ISE) and various SDNdomains (E.g. Datacenter, WAN, Campus) when intent is violated andprovide a notification to the operator(s) and administrator(s) ofrelevant domains based on business level intent being violated.

FIG. 7 shows a screen shot of an interface 700 showing allowedcross-domain access as part of providing cross-domain assurance. Inparticular the interface 700 shows that a student security group in aDNA network is allowed access to a grading portal in an ACI network butnot allowed access to a library portal in the ACI network. A networkadministrator can use this information to determine that a policymismatch is occurring. Specifically, the network administrator candetermine the ACI network is improperly configured by allowing a studentto access the grading portal and not the library portal, e.g. based onprior knowledge that students are allowed to access the library portalbut not the grading portal. Accordingly, the network administrator candetermine a policy mismatch exists based on this improper ACIconfiguration and subsequently reconfigure the ACI network.

The disclosure now turns to FIGS. 8 and 9, which illustrate examplenetwork devices and computing devices, such as switches, routers, loadbalancers, client devices, and so forth.

FIG. 8 illustrates an example network device 800 suitable for performingswitching, routing, load balancing, and other networking operations.Network device 800 includes a central processing unit (CPU) 804,interfaces 802, and a bus 810 (e.g., a PCI bus). When acting under thecontrol of appropriate software or firmware, the CPU 804 is responsiblefor executing packet management, error detection, and/or routingfunctions. The CPU 804 preferably accomplishes all these functions underthe control of software including an operating system and anyappropriate applications software. CPU 804 may include one or moreprocessors 808, such as a processor from the INTEL X86 family ofmicroprocessors. In some cases, processor 808 can be specially designedhardware for controlling the operations of network device 800. In somecases, a memory 806 (e.g., non-volatile RAM, ROM, etc.) also forms partof CPU 804. However, there are many different ways in which memory couldbe coupled to the system.

The interfaces 802 are typically provided as modular interface cards(sometimes referred to as “line cards”). Generally, they control thesending and receiving of data packets over the network and sometimessupport other peripherals used with the network device 800. Among theinterfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces, andthe like. In addition, various very high-speed interfaces may beprovided such as fast token ring interfaces, wireless interfaces,Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSIinterfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5Gcellular interfaces, CAN BUS, LoRA, and the like. Generally, theseinterfaces may include ports appropriate for communication with theappropriate media. In some cases, they may also include an independentprocessor and, in some instances, volatile RAM. The independentprocessors may control such communications intensive tasks as packetswitching, media control, signal processing, crypto processing, andmanagement. By providing separate processors for the communicationsintensive tasks, these interfaces allow the master microprocessor 804 toefficiently perform routing computations, network diagnostics, securityfunctions, etc.

Although the system shown in FIG. 8 is one specific network device ofthe present invention, it is by no means the only network devicearchitecture on which the present invention can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc., is often used.Further, other types of interfaces and media could also be used with thenetwork device 800.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 806) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc. Memory 806could also hold various software containers and virtualized executionenvironments and data.

The network device 800 can also include an application-specificintegrated circuit (ASIC), which can be configured to perform routingand/or switching operations. The ASIC can communicate with othercomponents in the network device 800 via the bus 810, to exchange dataand signals and coordinate various types of operations by the networkdevice 800, such as routing, switching, and/or data storage operations,for example.

FIG. 9 illustrates a computing system architecture 900 wherein thecomponents of the system are in electrical communication with each otherusing a connection 905, such as a bus. Exemplary system 900 includes aprocessing unit (CPU or processor) 910 and a system connection 905 thatcouples various system components including the system memory 915, suchas read only memory (ROM) 920 and random access memory (RAM) 925, to theprocessor 910. The system 900 can include a cache of high-speed memoryconnected directly with, in close proximity to, or integrated as part ofthe processor 910. The system 900 can copy data from the memory 915and/or the storage device 930 to the cache 912 for quick access by theprocessor 910. In this way, the cache can provide a performance boostthat avoids processor 910 delays while waiting for data. These and othermodules can control or be configured to control the processor 910 toperform various actions. Other system memory 915 may be available foruse as well. The memory 915 can include multiple different types ofmemory with different performance characteristics. The processor 910 caninclude any general purpose processor and a hardware or softwareservice, such as service 1 932, service 2 934, and service 3 936 storedin storage device 930, configured to control the processor 910 as wellas a special-purpose processor where software instructions areincorporated into the actual processor design. The processor 910 may bea completely self-contained computing system, containing multiple coresor processors, a bus, memory controller, cache, etc. A multi-coreprocessor may be symmetric or asymmetric.

To enable user interaction with the computing device 900, an inputdevice 945 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 935 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 900. The communications interface940 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 930 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 925, read only memory (ROM) 920, andhybrids thereof

The storage device 930 can include services 932, 934, 936 forcontrolling the processor 910. Other hardware or software modules arecontemplated. The storage device 930 can be connected to the systemconnection 905. In one aspect, a hardware module that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 910, connection 905, output device935, and so forth, to carry out the function.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer- executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of aset and indicates that one member of the set or multiple members of theset satisfy the claim. For example, claim language reciting “at leastone of A and B” means A, B, or A and B.

What is claimed is:
 1. A method, comprising: collecting fabric dataacross a plurality of network domains, wherein the fabric data indicatesone or more policies implemented in corresponding network domains of theplurality of network domains; translating at least a portion of thefabric data from at least one of the plurality of network domains into aform that is compatible with another portion of the fabric data from atleast another of the plurality of network domains to create translatedfabric data; correlating the fabric data, including at least the portionof the fabric data from at least one of the plurality of network domainsand the another portion of the fabric data from at least another of theplurality of network domains, based at least in part on the translatedfabric data to create correlated fabric data; and providing policyassurance across the plurality of network domains by determining whetherthe one or more policies implemented in corresponding network domains ofthe plurality of network domains are compatible across the plurality ofnetwork domains based on the correlated fabric data.
 2. The method ofclaim 1, wherein the at least one of the plurality of network domainsand the at least another of the plurality of network domains are in asame network environment.
 3. The method of claim 1, wherein the at leastone of the plurality of network domains is a virtualized network domain.4. The method of claim 1, further comprising: comparing policiesidentified from the translated fabric data across the plurality ofnetwork domains; and generating the correlated fabric data across theplurality of network domains based on a comparison of the policiesacross the plurality of network domains.
 5. The method of claim 4,further comprising: identifying one or more mismatches in the policiesacross the plurality of network domains; and generating the correlatedfabric data across the plurality of network domains based on the one ormore mismatches in the policies across the plurality of network domains.6. The method of claim 1, wherein one or more polices of the at leastone of the plurality of network domains are translated into one or morepolicies for communicating with either or both one or more endpoints andone or more endpoint groups.
 7. The method of claim 1, wherein one ormore polices of the at least one of the plurality of network domains aretranslated into one or more policies for communicating with one or moresecurity groups.
 8. A system comprising: one or more processors; and acomputer-readable medium comprising instructions stored therein, whichwhen executed by the one or more processors, cause the one or moreprocessors to: collect fabric data across a plurality of networkdomains, wherein the fabric data indicates one or more policiesimplemented in corresponding network domains of the plurality of networkdomains; translate at least a portion of the fabric data from at leastone of the plurality of network domains into a form that is compatiblewith another portion of the fabric data from at least another of theplurality of network domains to create translated fabric data; correlatethe fabric data, including at least the portion of the fabric data fromat least one of the plurality of network domains and the another portionof the fabric data from at least another of the plurality of networkdomains, based at least in part on the translated fabric data to createcorrelated fabric data; and provide policy assurance across theplurality of network domains by determining whether the one or morepolicies implemented in corresponding network domains of the pluralityof network domains are compatible across the plurality of networkdomains based on the correlated fabric data.
 9. The system of claim 8,wherein the at least one of the plurality of network domains and the atleast another of the plurality of network domains are in a same networkenvironment.
 10. The system of claim 8, wherein the at least one of theplurality of network domains is a virtualized network domain.
 11. Thesystem of claim 8, further comprising instructions, which when executedby the one or more processors, cause the one or more processors to:compare policies identified from the translated fabric data across theplurality of network domains; and generate the correlated fabric dataacross the plurality of network domains based on a comparison of thepolicies across the plurality of network domains.
 12. The system ofclaim 11, further comprising instructions, which when executed by theone or more processors, cause the one or more processors to: identifyone or more mismatches in the policies across the plurality of networkdomains; and generate the correlated fabric data across the plurality ofnetwork domains based on the one or more mismatches in the policiesacross the plurality of network domains.
 13. The system of claim 8,wherein one or more polices of the at least one of the plurality ofnetwork domains are translated into one or more policies forcommunicating with either or both one or more endpoints and one or moreendpoint groups.
 14. The system of claim 8, wherein one or more policesof the at least one of the plurality of network domains are translatedinto one or more policies for communicating with one or more securitygroups.
 15. At least one non-transitory computer-readable storage mediumcomprising instructions stored therein, which when executed by one ormore processors, cause the one or more processors to: collect fabricdata across a plurality of network domains, wherein the fabric dataindicates one or more policies implemented in corresponding networkdomains of the plurality of network domains; translate at least aportion of the fabric data from at least one of the plurality of networkdomains into a form that is compatible with another portion of thefabric data from at least another of the plurality of network domains tocreate translated fabric data; correlate the fabric data, including atleast the portion of the fabric data from at least one of the pluralityof network domains and the another portion of the fabric data from atleast another of the plurality of network domains, based at least inpart on the translated fabric data to create correlated fabric data; andprovide policy assurance across the plurality of network domains bydetermining whether the one or more policies implemented incorresponding network domains of the plurality of network domains arecompatible across the plurality of network domains based on thecorrelated fabric data.
 16. The at least one non-transitorycomputer-readable storage medium of claim 15, wherein the at least oneof the plurality of network domains and the at least another of theplurality of network domains are in a same network environment.
 17. Theat least one non-transitory computer-readable storage medium of claim15, wherein the at least one of the plurality of network domains is avirtualized network domain.
 18. The at least one non-transitorycomputer-readable storage medium of claim 15, further comprisinginstructions, which when executed by the one or more processors, causethe one or more processors to: compare policies identified from thetranslated fabric data across the plurality of network domains; andgenerate the correlated fabric data across the plurality of networkdomains based on a comparison of the policies across the plurality ofnetwork domains.
 19. The at least one non-transitory computer-readablestorage medium of claim 18, further comprising instructions, which whenexecuted by the one or more processors, cause the one or more processorsto: identify one or more mismatches in the policies across the pluralityof network domains; and generate the correlated fabric data across theplurality of network domains based on the one or more mismatches in thepolicies across the plurality of network domains.
 20. The at least onenon-transitory computer-readable storage medium of claim 15, wherein oneor more polices of the at least one of the plurality of network domainsare translated into one or more policies for communicating with eitheror both one or more endpoints and one or more endpoint groups.